Sentence Similarity
sentence-transformers
PyTorch
Transformers
Chinese
bert
feature-extraction
semantic-search
chinese
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use DMetaSoul/sbert-chinese-general-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DMetaSoul/sbert-chinese-general-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DMetaSoul/sbert-chinese-general-v1") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use DMetaSoul/sbert-chinese-general-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMetaSoul/sbert-chinese-general-v1") model = AutoModel.from_pretrained("DMetaSoul/sbert-chinese-general-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DMetaSoul/sbert-chinese-general-v1: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/DMetaSoul/sbert-chinese-general-v1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DMetaSoul/sbert-chinese-general-v1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DMetaSoul/sbert-chinese-general-v1/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- e3148c1122f50df213c4bc87c8b582bf1fb66ec842c9cde9568bac8164071d15
- Size of remote file:
- 409 MB
- SHA256:
- 36b66c4b032c27b57ae4dacd2a93dc6ff1c4cc8c028a48c340f36c408e7bffdd
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